OEKernelPLS

class OEKernelPLS

The OEKernelPLS can be used to build models using kernel partial least squares technique (kPLS).

To train a model using kPLS, the descriptors are expected to be in the dot-kernel space. Kernel partial least squares (PLS) has been particularly popular in chemometrics, due to its sub-cubic runtime for learning, and an iterative construction of directions which are relevant for predicting the outputs.

The OEKernelPLS class defines the following public methods:

• Fit

• GetB0

• GetBValues

• GetNumFeaturesUsed

• Predict

Constructor

OEKernelPLS() -> OEKernelPLS
OEKernelPLS(arg2: OEKernelPLS) -> OEKernelPLS

Default and copy constructors.

Fit

Fit(kernel: OESquareMatrix, vecResponse: OEDoubleVector,
    maxFeatures: int) -> bool

Fit model using the provided kernel descriptor matrix.

kernel

kernel descriptor matrix.

vecResponse

vector of response corresponding to descriptors.

maxFeatures

Maximum number of PLS features to use for model fitting. A value of 0 (zero) corresponds to choosing number of features to fit the best model that minimizes error on the training set.

GetB0

GetB0() -> float

Get the fitted model intercept.

GetBValues

GetBValues() -> OEDoubleVector

Get the fitted model regression coefficients.

GetNumFeaturesUsed

GetNumFeaturesUsed() -> int

Returns the actual number of PLS features used for model fitting.

Predict

Predict(kernel: OEDoubleVector) -> float

Returns predicted estimation for the input descriptor vector.